org.numenta.nupic.algorithms.SpatialPooler Maven / Gradle / Ivy
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package org.numenta.nupic.algorithms;
import java.util.Arrays;
import java.util.stream.IntStream;
import org.numenta.nupic.model.Column;
import org.numenta.nupic.model.Connections;
import org.numenta.nupic.model.Persistable;
import org.numenta.nupic.model.Pool;
import org.numenta.nupic.util.ArrayUtils;
import org.numenta.nupic.util.Condition;
import org.numenta.nupic.util.SparseBinaryMatrix;
import org.numenta.nupic.util.SparseMatrix;
import org.numenta.nupic.util.SparseObjectMatrix;
import org.numenta.nupic.util.Topology;
import chaschev.lang.Pair;
import gnu.trove.list.TIntList;
import gnu.trove.list.array.TDoubleArrayList;
import gnu.trove.list.array.TIntArrayList;
import gnu.trove.set.hash.TIntHashSet;
/**
* Handles the relationships between the columns of a region
* and the inputs bits. The primary public interface to this function is the
* "compute" method, which takes in an input vector and returns a list of
* activeColumns columns.
* Example Usage:
* >
* > SpatialPooler sp = SpatialPooler();
* > Connections c = new Connections();
* > sp.init(c);
* > for line in file:
* > inputVector = prepared int[] (containing 1's and 0's)
* > sp.compute(inputVector)
*
* @author David Ray
*
*/
public class SpatialPooler implements Persistable {
/** Default Serial Version */
private static final long serialVersionUID = 1L;
/**
* Constructs a new {@code SpatialPooler}
*/
public SpatialPooler() {}
/**
* Initializes the specified {@link Connections} object which contains
* the memory and structural anatomy this spatial pooler uses to implement
* its algorithms.
*
* @param c a {@link Connections} object
*/
public void init(Connections c) {
if(c.getNumActiveColumnsPerInhArea() == 0 && (c.getLocalAreaDensity() == 0 ||
c.getLocalAreaDensity() > 0.5)) {
throw new InvalidSPParamValueException("Inhibition parameters are invalid");
}
c.doSpatialPoolerPostInit();
initMatrices(c);
connectAndConfigureInputs(c);
}
/**
* Called to initialize the structural anatomy with configured values and prepare
* the anatomical entities for activation.
*
* @param c
*/
public void initMatrices(final Connections c) {
SparseObjectMatrix mem = c.getMemory();
c.setMemory(mem == null ?
mem = new SparseObjectMatrix<>(c.getColumnDimensions()) : mem);
c.setInputMatrix(new SparseBinaryMatrix(c.getInputDimensions()));
// Initiate the topologies
c.setColumnTopology(new Topology(c.getColumnDimensions()));
c.setInputTopology(new Topology(c.getInputDimensions()));
//Calculate numInputs and numColumns
int numInputs = c.getInputMatrix().getMaxIndex() + 1;
int numColumns = c.getMemory().getMaxIndex() + 1;
if(numColumns <= 0) {
throw new InvalidSPParamValueException("Invalid number of columns: " + numColumns);
}
if(numInputs <= 0) {
throw new InvalidSPParamValueException("Invalid number of inputs: " + numInputs);
}
c.setNumInputs(numInputs);
c.setNumColumns(numColumns);
//Fill the sparse matrix with column objects
for(int i = 0;i < numColumns;i++) { mem.set(i, new Column(c.getCellsPerColumn(), i)); }
c.setPotentialPools(new SparseObjectMatrix(c.getMemory().getDimensions()));
c.setConnectedMatrix(new SparseBinaryMatrix(new int[] { numColumns, numInputs }));
//Initialize state meta-management statistics
c.setOverlapDutyCycles(new double[numColumns]);
c.setActiveDutyCycles(new double[numColumns]);
c.setMinOverlapDutyCycles(new double[numColumns]);
c.setMinActiveDutyCycles(new double[numColumns]);
c.setBoostFactors(new double[numColumns]);
Arrays.fill(c.getBoostFactors(), 1);
}
/**
* Step two of pooler initialization kept separate from initialization
* of static members so that they may be set at a different point in
* the initialization (as sometimes needed by tests).
*
* This step prepares the proximal dendritic synapse pools with their
* initial permanence values and connected inputs.
*
* @param c the {@link Connections} memory
*/
public void connectAndConfigureInputs(Connections c) {
// Initialize the set of permanence values for each column. Ensure that
// each column is connected to enough input bits to allow it to be
// activated.
int numColumns = c.getNumColumns();
for(int i = 0;i < numColumns;i++) {
int[] potential = mapPotential(c, i, c.isWrapAround());
Column column = c.getColumn(i);
c.getPotentialPools().set(i, column.createPotentialPool(c, potential));
double[] perm = initPermanence(c, potential, i, c.getInitConnectedPct());
updatePermanencesForColumn(c, perm, column, potential, true);
}
// The inhibition radius determines the size of a column's local
// neighborhood. A cortical column must overcome the overlap score of
// columns in its neighborhood in order to become active. This radius is
// updated every learning round. It grows and shrinks with the average
// number of connected synapses per column.
updateInhibitionRadius(c);
}
/**
* This is the primary public method of the SpatialPooler class. This
* function takes a input vector and outputs the indices of the active columns.
* If 'learn' is set to True, this method also updates the permanences of the
* columns.
* @param inputVector An array of 0's and 1's that comprises the input to
* the spatial pooler. The array will be treated as a one
* dimensional array, therefore the dimensions of the array
* do not have to match the exact dimensions specified in the
* class constructor. In fact, even a list would suffice.
* The number of input bits in the vector must, however,
* match the number of bits specified by the call to the
* constructor. Therefore there must be a '0' or '1' in the
* array for every input bit.
* @param activeArray An array whose size is equal to the number of columns.
* Before the function returns this array will be populated
* with 1's at the indices of the active columns, and 0's
* everywhere else.
* @param learn A boolean value indicating whether learning should be
* performed. Learning entails updating the permanence
* values of the synapses, and hence modifying the 'state'
* of the model. Setting learning to 'off' freezes the SP
* and has many uses. For example, you might want to feed in
* various inputs and examine the resulting SDR's.
*/
public void compute(Connections c, int[] inputVector, int[] activeArray, boolean learn) {
if(inputVector.length != c.getNumInputs()) {
throw new InvalidSPParamValueException(
"Input array must be same size as the defined number of inputs: From Params: " + c.getNumInputs() +
", From Input Vector: " + inputVector.length);
}
updateBookeepingVars(c, learn);
int[] overlaps = c.setOverlaps(calculateOverlap(c, inputVector));
double[] boostedOverlaps;
if(learn) {
boostedOverlaps = ArrayUtils.multiply(c.getBoostFactors(), overlaps);
}else{
boostedOverlaps = ArrayUtils.toDoubleArray(overlaps);
}
int[] activeColumns = inhibitColumns(c, c.setBoostedOverlaps(boostedOverlaps));
if(learn) {
adaptSynapses(c, inputVector, activeColumns);
updateDutyCycles(c, overlaps, activeColumns);
bumpUpWeakColumns(c);
updateBoostFactors(c);
if(isUpdateRound(c)) {
updateInhibitionRadius(c);
updateMinDutyCycles(c);
}
}
Arrays.fill(activeArray, 0);
if(activeColumns.length > 0) {
ArrayUtils.setIndexesTo(activeArray, activeColumns, 1);
}
}
/**
* Removes the set of columns who have never been active from the set of
* active columns selected in the inhibition round. Such columns cannot
* represent learned pattern and are therefore meaningless if only inference
* is required. This should not be done when using a random, unlearned SP
* since you would end up with no active columns.
*
* @param activeColumns An array containing the indices of the active columns
* @return a list of columns with a chance of activation
*/
public int[] stripUnlearnedColumns(Connections c, int[] activeColumns) {
TIntHashSet active = new TIntHashSet(activeColumns);
TIntHashSet aboveZero = new TIntHashSet();
int numCols = c.getNumColumns();
double[] colDutyCycles = c.getActiveDutyCycles();
for(int i = 0;i < numCols;i++) {
if(colDutyCycles[i] <= 0) {
aboveZero.add(i);
}
}
active.removeAll(aboveZero);
TIntArrayList l = new TIntArrayList(active);
l.sort();
return Arrays.stream(activeColumns).filter(i -> c.getActiveDutyCycles()[i] > 0).toArray();
}
/**
* Updates the minimum duty cycles defining normal activity for a column. A
* column with activity duty cycle below this minimum threshold is boosted.
*
* @param c
*/
public void updateMinDutyCycles(Connections c) {
if(c.getGlobalInhibition() || c.getInhibitionRadius() > c.getNumInputs()) {
updateMinDutyCyclesGlobal(c);
}else{
updateMinDutyCyclesLocal(c);
}
}
/**
* Updates the minimum duty cycles in a global fashion. Sets the minimum duty
* cycles for the overlap and activation of all columns to be a percent of the
* maximum in the region, specified by {@link Connections#getMinOverlapDutyCycles()} and
* minPctActiveDutyCycle respectively. Functionality it is equivalent to
* {@link #updateMinDutyCyclesLocal(Connections)}, but this function exploits the globalness of the
* computation to perform it in a straightforward, and more efficient manner.
*
* @param c
*/
public void updateMinDutyCyclesGlobal(Connections c) {
Arrays.fill(c.getMinOverlapDutyCycles(),
c.getMinPctOverlapDutyCycles() * ArrayUtils.max(c.getOverlapDutyCycles()));
Arrays.fill(c.getMinActiveDutyCycles(),
c.getMinPctActiveDutyCycles() * ArrayUtils.max(c.getActiveDutyCycles()));
}
/**
* Updates the minimum duty cycles. The minimum duty cycles are determined
* locally. Each column's minimum duty cycles are set to be a percent of the
* maximum duty cycles in the column's neighborhood. Unlike
* {@link #updateMinDutyCyclesGlobal(Connections)}, here the values can be
* quite different for different columns.
*
* @param c
*/
public void updateMinDutyCyclesLocal(final Connections c) {
int len = c.getNumColumns();
int inhibitionRadius = c.getInhibitionRadius();
double[] activeDutyCycles = c.getActiveDutyCycles();
double minPctActiveDutyCycles = c.getMinPctActiveDutyCycles();
double[] overlapDutyCycles = c.getOverlapDutyCycles();
double minPctOverlapDutyCycles = c.getMinPctOverlapDutyCycles();
// Parallelize for speed up
IntStream.range(0, len).forEach(i -> {
int[] neighborhood = getColumnNeighborhood(c, i, inhibitionRadius);
double maxActiveDuty = ArrayUtils.max(
ArrayUtils.sub(activeDutyCycles, neighborhood));
double maxOverlapDuty = ArrayUtils.max(
ArrayUtils.sub(overlapDutyCycles, neighborhood));
c.getMinActiveDutyCycles()[i] = maxActiveDuty * minPctActiveDutyCycles;
c.getMinOverlapDutyCycles()[i] = maxOverlapDuty * minPctOverlapDutyCycles;
});
}
/**
* Updates the duty cycles for each column. The OVERLAP duty cycle is a moving
* average of the number of inputs which overlapped with each column. The
* ACTIVITY duty cycles is a moving average of the frequency of activation for
* each column.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param overlaps an array containing the overlap score for each column.
* The overlap score for a column is defined as the number
* of synapses in a "connected state" (connected synapses)
* that are connected to input bits which are turned on.
* @param activeColumns An array containing the indices of the active columns,
* the sparse set of columns which survived inhibition
*/
public void updateDutyCycles(Connections c, int[] overlaps, int[] activeColumns) {
double[] overlapArray = new double[c.getNumColumns()];
double[] activeArray = new double[c.getNumColumns()];
ArrayUtils.greaterThanXThanSetToYInB(overlaps, overlapArray, 0, 1);
if(activeColumns.length > 0) {
ArrayUtils.setIndexesTo(activeArray, activeColumns, 1);
}
int period = c.getDutyCyclePeriod();
if(period > c.getIterationNum()) {
period = c.getIterationNum();
}
c.setOverlapDutyCycles(
updateDutyCyclesHelper(c, c.getOverlapDutyCycles(), overlapArray, period));
c.setActiveDutyCycles(
updateDutyCyclesHelper(c, c.getActiveDutyCycles(), activeArray, period));
}
/**
* Updates a duty cycle estimate with a new value. This is a helper
* function that is used to update several duty cycle variables in
* the Column class, such as: overlapDutyCucle, activeDutyCycle,
* minPctDutyCycleBeforeInh, minPctDutyCycleAfterInh, etc. returns
* the updated duty cycle. Duty cycles are updated according to the following
* formula:
*
*
* (period - 1)*dutyCycle + newValue
* dutyCycle := ----------------------------------
* period
*
* @param c the {@link Connections} (spatial pooler memory)
* @param dutyCycles An array containing one or more duty cycle values that need
* to be updated
* @param newInput A new numerical value used to update the duty cycle
* @param period The period of the duty cycle
* @return
*/
public double[] updateDutyCyclesHelper(Connections c, double[] dutyCycles, double[] newInput, double period) {
return ArrayUtils.divide(ArrayUtils.d_add(ArrayUtils.multiply(dutyCycles, period - 1), newInput), period);
}
/**
* Update the inhibition radius. The inhibition radius is a measure of the
* square (or hypersquare) of columns that each a column is "connected to"
* on average. Since columns are are not connected to each other directly, we
* determine this quantity by first figuring out how many *inputs* a column is
* connected to, and then multiplying it by the total number of columns that
* exist for each input. For multiple dimension the aforementioned
* calculations are averaged over all dimensions of inputs and columns. This
* value is meaningless if global inhibition is enabled.
*
* @param c the {@link Connections} (spatial pooler memory)
*/
public void updateInhibitionRadius(Connections c) {
if(c.getGlobalInhibition()) {
c.setInhibitionRadius(ArrayUtils.max(c.getColumnDimensions()));
return;
}
TDoubleArrayList avgCollected = new TDoubleArrayList();
int len = c.getNumColumns();
for(int i = 0;i < len;i++) {
avgCollected.add(avgConnectedSpanForColumnND(c, i));
}
double avgConnectedSpan = ArrayUtils.average(avgCollected.toArray());
double diameter = avgConnectedSpan * avgColumnsPerInput(c);
double radius = (diameter - 1) / 2.0d;
radius = Math.max(1, radius);
c.setInhibitionRadius((int)(radius + 0.5));
}
/**
* The average number of columns per input, taking into account the topology
* of the inputs and columns. This value is used to calculate the inhibition
* radius. This function supports an arbitrary number of dimensions. If the
* number of column dimensions does not match the number of input dimensions,
* we treat the missing, or phantom dimensions as 'ones'.
*
* @param c the {@link Connections} (spatial pooler memory)
* @return
*/
public double avgColumnsPerInput(Connections c) {
int[] colDim = Arrays.copyOf(c.getColumnDimensions(), c.getColumnDimensions().length);
int[] inputDim = Arrays.copyOf(c.getInputDimensions(), c.getInputDimensions().length);
double[] columnsPerInput = ArrayUtils.divide(
ArrayUtils.toDoubleArray(colDim), ArrayUtils.toDoubleArray(inputDim), 0, 0);
return ArrayUtils.average(columnsPerInput);
}
/**
* The range of connectedSynapses per column, averaged for each dimension.
* This value is used to calculate the inhibition radius. This variation of
* the function supports arbitrary column dimensions.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param columnIndex the current column for which to avg.
* @return
*/
public double avgConnectedSpanForColumnND(Connections c, int columnIndex) {
int[] dimensions = c.getInputDimensions();
int[] connected = c.getColumn(columnIndex).getProximalDendrite().getConnectedSynapsesSparse(c);
if(connected == null || connected.length == 0) return 0;
int[] maxCoord = new int[c.getInputDimensions().length];
int[] minCoord = new int[c.getInputDimensions().length];
Arrays.fill(maxCoord, -1);
Arrays.fill(minCoord, ArrayUtils.max(dimensions));
SparseMatrix inputMatrix = c.getInputMatrix();
for(int i = 0;i < connected.length;i++) {
maxCoord = ArrayUtils.maxBetween(maxCoord, inputMatrix.computeCoordinates(connected[i]));
minCoord = ArrayUtils.minBetween(minCoord, inputMatrix.computeCoordinates(connected[i]));
}
return ArrayUtils.average(ArrayUtils.add(ArrayUtils.subtract(maxCoord, minCoord), 1));
}
/**
* The primary method in charge of learning. Adapts the permanence values of
* the synapses based on the input vector, and the chosen columns after
* inhibition round. Permanence values are increased for synapses connected to
* input bits that are turned on, and decreased for synapses connected to
* inputs bits that are turned off.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param inputVector a integer array that comprises the input to
* the spatial pooler. There exists an entry in the array
* for every input bit.
* @param activeColumns an array containing the indices of the columns that
* survived inhibition.
*/
public void adaptSynapses(Connections c, int[] inputVector, int[] activeColumns) {
int[] inputIndices = ArrayUtils.where(inputVector, ArrayUtils.INT_GREATER_THAN_0);
double[] permChanges = new double[c.getNumInputs()];
Arrays.fill(permChanges, -1 * c.getSynPermInactiveDec());
ArrayUtils.setIndexesTo(permChanges, inputIndices, c.getSynPermActiveInc());
for(int i = 0;i < activeColumns.length;i++) {
Pool pool = c.getPotentialPools().get(activeColumns[i]);
double[] perm = pool.getDensePermanences(c);
int[] indexes = pool.getSparsePotential();
ArrayUtils.raiseValuesBy(permChanges, perm);
Column col = c.getColumn(activeColumns[i]);
updatePermanencesForColumn(c, perm, col, indexes, true);
}
}
/**
* This method increases the permanence values of synapses of columns whose
* activity level has been too low. Such columns are identified by having an
* overlap duty cycle that drops too much below those of their peers. The
* permanence values for such columns are increased.
*
* @param c
*/
public void bumpUpWeakColumns(final Connections c) {
int[] weakColumns = ArrayUtils.where(c.getMemory().get1DIndexes(), new Condition.Adapter() {
@Override public boolean eval(int i) {
return c.getOverlapDutyCycles()[i] < c.getMinOverlapDutyCycles()[i];
}
});
for(int i = 0;i < weakColumns.length;i++) {
Pool pool = c.getPotentialPools().get(weakColumns[i]);
double[] perm = pool.getSparsePermanences();
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm);
int[] indexes = pool.getSparsePotential();
Column col = c.getColumn(weakColumns[i]);
updatePermanencesForColumnSparse(c, perm, col, indexes, true);
}
}
/**
* This method ensures that each column has enough connections to input bits
* to allow it to become active. Since a column must have at least
* 'stimulusThreshold' overlaps in order to be considered during the
* inhibition phase, columns without such minimal number of connections, even
* if all the input bits they are connected to turn on, have no chance of
* obtaining the minimum threshold. For such columns, the permanence values
* are increased until the minimum number of connections are formed.
*
* @param c the {@link Connections} memory
* @param perm the permanence values
* @param maskPotential
*/
public void raisePermanenceToThreshold(Connections c, double[] perm, int[] maskPotential) {
if(maskPotential.length < c.getStimulusThreshold()) {
throw new IllegalStateException("This is likely due to a " +
"value of stimulusThreshold that is too large relative " +
"to the input size. [len(mask) < self._stimulusThreshold]");
}
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
while(true) {
int numConnected = ArrayUtils.valueGreaterCountAtIndex(c.getSynPermConnected(), perm, maskPotential);
if(numConnected >= c.getStimulusThreshold()) return;
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm, maskPotential);
}
}
/**
* This method ensures that each column has enough connections to input bits
* to allow it to become active. Since a column must have at least
* 'stimulusThreshold' overlaps in order to be considered during the
* inhibition phase, columns without such minimal number of connections, even
* if all the input bits they are connected to turn on, have no chance of
* obtaining the minimum threshold. For such columns, the permanence values
* are increased until the minimum number of connections are formed.
*
* Note: This method services the "sparse" versions of corresponding methods
*
* @param c The {@link Connections} memory
* @param perm permanence values
*/
public void raisePermanenceToThresholdSparse(Connections c, double[] perm) {
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
while(true) {
int numConnected = ArrayUtils.valueGreaterCount(c.getSynPermConnected(), perm);
if(numConnected >= c.getStimulusThreshold()) return;
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm);
}
}
/**
* This method updates the permanence matrix with a column's new permanence
* values. The column is identified by its index, which reflects the row in
* the matrix, and the permanence is given in 'sparse' form, i.e. an array
* whose members are associated with specific indexes. It is in
* charge of implementing 'clipping' - ensuring that the permanence values are
* always between 0 and 1 - and 'trimming' - enforcing sparseness by zeroing out
* all permanence values below 'synPermTrimThreshold'. It also maintains
* the consistency between 'permanences' (the matrix storing the
* permanence values), 'connectedSynapses', (the matrix storing the bits
* each column is connected to), and 'connectedCounts' (an array storing
* the number of input bits each column is connected to). Every method wishing
* to modify the permanence matrix should do so through this method.
*
* @param c the {@link Connections} which is the memory model.
* @param perm An array of permanence values for a column. The array is
* "dense", i.e. it contains an entry for each input bit, even
* if the permanence value is 0.
* @param column The column in the permanence, potential and connectivity matrices
* @param maskPotential The indexes of inputs in the specified {@link Column}'s pool.
* @param raisePerm a boolean value indicating whether the permanence values
*/
public void updatePermanencesForColumn(Connections c, double[] perm, Column column, int[] maskPotential, boolean raisePerm) {
if(raisePerm) {
raisePermanenceToThreshold(c, perm, maskPotential);
}
ArrayUtils.lessThanOrEqualXThanSetToY(perm, c.getSynPermTrimThreshold(), 0);
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
column.setProximalPermanences(c, perm);
}
/**
* This method updates the permanence matrix with a column's new permanence
* values. The column is identified by its index, which reflects the row in
* the matrix, and the permanence is given in 'sparse' form, (i.e. an array
* whose members are associated with specific indexes). It is in
* charge of implementing 'clipping' - ensuring that the permanence values are
* always between 0 and 1 - and 'trimming' - enforcing sparseness by zeroing out
* all permanence values below 'synPermTrimThreshold'. Every method wishing
* to modify the permanence matrix should do so through this method.
*
* @param c the {@link Connections} which is the memory model.
* @param perm An array of permanence values for a column. The array is
* "sparse", i.e. it contains an entry for each input bit, even
* if the permanence value is 0.
* @param column The column in the permanence, potential and connectivity matrices
* @param raisePerm a boolean value indicating whether the permanence values
*/
public void updatePermanencesForColumnSparse(Connections c, double[] perm, Column column, int[] maskPotential, boolean raisePerm) {
if(raisePerm) {
raisePermanenceToThresholdSparse(c, perm);
}
ArrayUtils.lessThanOrEqualXThanSetToY(perm, c.getSynPermTrimThreshold(), 0);
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
column.setProximalPermanencesSparse(c, perm, maskPotential);
}
/**
* Returns a randomly generated permanence value for a synapse that is
* initialized in a connected state. The basic idea here is to initialize
* permanence values very close to synPermConnected so that a small number of
* learning steps could make it disconnected or connected.
*
* Note: experimentation was done a long time ago on the best way to initialize
* permanence values, but the history for this particular scheme has been lost.
*
* @return a randomly generated permanence value
*/
public static double initPermConnected(Connections c) {
double p = c.getSynPermConnected() + (c.getSynPermMax() - c.getSynPermConnected()) * c.random.nextDouble();
// Note from Python implementation on conditioning below:
// Ensure we don't have too much unnecessary precision. A full 64 bits of
// precision causes numerical stability issues across platforms and across
// implementations
p = ((int)(p * 100000)) / 100000.0d;
return p;
}
/**
* Returns a randomly generated permanence value for a synapses that is to be
* initialized in a non-connected state.
*
* @return a randomly generated permanence value
*/
public static double initPermNonConnected(Connections c) {
double p = c.getSynPermConnected() * c.getRandom().nextDouble();
// Note from Python implementation on conditioning below:
// Ensure we don't have too much unnecessary precision. A full 64 bits of
// precision causes numerical stability issues across platforms and across
// implementations
p = ((int)(p * 100000)) / 100000.0d;
return p;
}
/**
* Initializes the permanences of a column. The method
* returns a 1-D array the size of the input, where each entry in the
* array represents the initial permanence value between the input bit
* at the particular index in the array, and the column represented by
* the 'index' parameter.
*
* @param c the {@link Connections} which is the memory model
* @param potentialPool An array specifying the potential pool of the column.
* Permanence values will only be generated for input bits
* corresponding to indices for which the mask value is 1.
* WARNING: potentialPool is sparse, not an array of "1's"
* @param index the index of the column being initialized
* @param connectedPct A value between 0 or 1 specifying the percent of the input
* bits that will start off in a connected state.
* @return
*/
public double[] initPermanence(Connections c, int[] potentialPool, int index, double connectedPct) {
double[] perm = new double[c.getNumInputs()];
for(int idx : potentialPool) {
if(c.random.nextDouble() <= connectedPct) {
perm[idx] = initPermConnected(c);
}else{
perm[idx] = initPermNonConnected(c);
}
perm[idx] = perm[idx] < c.getSynPermTrimThreshold() ? 0 : perm[idx];
}
c.getColumn(index).setProximalPermanences(c, perm);
return perm;
}
/**
* Maps a column to its respective input index, keeping to the topology of
* the region. It takes the index of the column as an argument and determines
* what is the index of the flattened input vector that is to be the center of
* the column's potential pool. It distributes the columns over the inputs
* uniformly. The return value is an integer representing the index of the
* input bit. Examples of the expected output of this method:
* * If the topology is one dimensional, and the column index is 0, this
* method will return the input index 0. If the column index is 1, and there
* are 3 columns over 7 inputs, this method will return the input index 3.
* * If the topology is two dimensional, with column dimensions [3, 5] and
* input dimensions [7, 11], and the column index is 3, the method
* returns input index 8.
*
* @param columnIndex The index identifying a column in the permanence, potential
* and connectivity matrices.
* @return A boolean value indicating that boundaries should be
* ignored.
*/
public int mapColumn(Connections c, int columnIndex) {
int[] columnCoords = c.getMemory().computeCoordinates(columnIndex);
double[] colCoords = ArrayUtils.toDoubleArray(columnCoords);
double[] ratios = ArrayUtils.divide(
colCoords, ArrayUtils.toDoubleArray(c.getColumnDimensions()), 0, 0);
double[] inputCoords = ArrayUtils.multiply(
ArrayUtils.toDoubleArray(c.getInputDimensions()), ratios, 0, 0);
inputCoords = ArrayUtils.d_add(
inputCoords,
ArrayUtils.multiply(
ArrayUtils.divide(
ArrayUtils.toDoubleArray(c.getInputDimensions()),
ArrayUtils.toDoubleArray(c.getColumnDimensions()), 0, 0),
0.5));
int[] inputCoordInts = ArrayUtils.clip(ArrayUtils.toIntArray(inputCoords), c.getInputDimensions(), -1);
return c.getInputMatrix().computeIndex(inputCoordInts);
}
/**
* Maps a column to its input bits. This method encapsulates the topology of
* the region. It takes the index of the column as an argument and determines
* what are the indices of the input vector that are located within the
* column's potential pool. The return value is a list containing the indices
* of the input bits. The current implementation of the base class only
* supports a 1 dimensional topology of columns with a 1 dimensional topology
* of inputs. To extend this class to support 2-D topology you will need to
* override this method. Examples of the expected output of this method:
* * If the potentialRadius is greater than or equal to the entire input
* space, (global visibility), then this method returns an array filled with
* all the indices
* * If the topology is one dimensional, and the potentialRadius is 5, this
* method will return an array containing 5 consecutive values centered on
* the index of the column (wrapping around if necessary).
* * If the topology is two dimensional (not implemented), and the
* potentialRadius is 5, the method should return an array containing 25
* '1's, where the exact indices are to be determined by the mapping from
* 1-D index to 2-D position.
*
* @param c {@link Connections} the main memory model
* @param columnIndex The index identifying a column in the permanence, potential
* and connectivity matrices.
* @param wrapAround A boolean value indicating that boundaries should be
* ignored.
* @return
*/
public int[] mapPotential(Connections c, int columnIndex, boolean wrapAround) {
int centerInput = mapColumn(c, columnIndex);
int[] columnInputs = getInputNeighborhood(c, centerInput, c.getPotentialRadius());
// Select a subset of the receptive field to serve as the
// the potential pool
int numPotential = (int)(columnInputs.length * c.getPotentialPct() + 0.5);
int[] retVal = new int[numPotential];
return ArrayUtils.sample(columnInputs, retVal, c.getRandom());
}
/**
* Performs inhibition. This method calculates the necessary values needed to
* actually perform inhibition and then delegates the task of picking the
* active columns to helper functions.
*
* @param c the {@link Connections} matrix
* @param overlaps an array containing the overlap score for each column.
* The overlap score for a column is defined as the number
* of synapses in a "connected state" (connected synapses)
* that are connected to input bits which are turned on.
* @return
*/
public int[] inhibitColumns(Connections c, double[] overlaps) {
overlaps = Arrays.copyOf(overlaps, overlaps.length);
double density;
double inhibitionArea;
if((density = c.getLocalAreaDensity()) <= 0) {
inhibitionArea = Math.pow(2 * c.getInhibitionRadius() + 1, c.getColumnDimensions().length);
inhibitionArea = Math.min(c.getNumColumns(), inhibitionArea);
density = c.getNumActiveColumnsPerInhArea() / inhibitionArea;
density = Math.min(density, 0.5);
}
//Add our fixed little bit of random noise to the scores to help break ties.
//ArrayUtils.d_add(overlaps, c.getTieBreaker());
if(c.getGlobalInhibition() || c.getInhibitionRadius() > ArrayUtils.max(c.getColumnDimensions())) {
return inhibitColumnsGlobal(c, overlaps, density);
}
return inhibitColumnsLocal(c, overlaps, density);
}
/**
* Perform global inhibition. Performing global inhibition entails picking the
* top 'numActive' columns with the highest overlap score in the entire
* region. At most half of the columns in a local neighborhood are allowed to
* be active.
*
* @param c the {@link Connections} matrix
* @param overlaps an array containing the overlap score for each column.
* The overlap score for a column is defined as the number
* of synapses in a "connected state" (connected synapses)
* that are connected to input bits which are turned on.
* @param density The fraction of columns to survive inhibition.
*
* @return
*/
public int[] inhibitColumnsGlobal(Connections c, double[] overlaps, double density) {
int numCols = c.getNumColumns();
int numActive = (int)(density * numCols);
int[] sortedWinnerIndices = IntStream.range(0,overlaps.length)
.mapToObj(i-> new Pair<>(i,overlaps[i]))
.sorted(c.inhibitionComparator)
.mapToInt(Pair::getFirst)
.toArray();
// Enforce the stimulus threshold
double stimulusThreshold = c.getStimulusThreshold();
int start = sortedWinnerIndices.length - numActive;
while(start < sortedWinnerIndices.length) {
int i = sortedWinnerIndices[start];
if(overlaps[i] >= stimulusThreshold) break;
++start;
}
return IntStream.of(sortedWinnerIndices).skip(start).toArray();
}
/**
* Performs inhibition. This method calculates the necessary values needed to
* actually perform inhibition and then delegates the task of picking the
* active columns to helper functions.
*
* @param c the {@link Connections} matrix
* @param overlaps an array containing the overlap score for each column.
* The overlap score for a column is defined as the number
* of synapses in a "connected state" (connected synapses)
* that are connected to input bits which are turned on.
* @param density The fraction of columns to survive inhibition. This
* value is only an intended target. Since the surviving
* columns are picked in a local fashion, the exact fraction
* of surviving columns is likely to vary.
* @return indices of the winning columns
*/
public int[] inhibitColumnsLocal(Connections c, double[] overlaps, double density) {
double addToWinners = ArrayUtils.max(overlaps) / 1000.0d;
if(addToWinners == 0) {
addToWinners = 0.001;
}
double[] tieBrokenOverlaps = Arrays.copyOf(overlaps, overlaps.length);
TIntList winners = new TIntArrayList();
double stimulusThreshold = c.getStimulusThreshold();
int inhibitionRadius = c.getInhibitionRadius();
for(int i = 0;i < overlaps.length;i++) {
int column = i;
if(overlaps[column] >= stimulusThreshold) {
int[] neighborhood = getColumnNeighborhood(c, column, inhibitionRadius);
double[] neighborhoodOverlaps = ArrayUtils.sub(tieBrokenOverlaps, neighborhood);
long numBigger = Arrays.stream(neighborhoodOverlaps)
.parallel()
.filter(d -> d > overlaps[column])
.count();
int numActive = (int)(0.5 + density * neighborhood.length);
if(numBigger < numActive) {
winners.add(column);
tieBrokenOverlaps[column] += addToWinners;
}
}
}
return winners.toArray();
}
/**
* Update the boost factors for all columns. The boost factors are used to
* increase the overlap of inactive columns to improve their chances of
* becoming active. and hence encourage participation of more columns in the
* learning process. This is a line defined as: y = mx + b boost =
* (1-maxBoost)/minDuty * dutyCycle + maxFiringBoost. Intuitively this means
* that columns that have been active enough have a boost factor of 1, meaning
* their overlap is not boosted. Columns whose active duty cycle drops too much
* below that of their neighbors are boosted depending on how infrequently they
* have been active. The more infrequent, the more they are boosted. The exact
* boost factor is linearly interpolated between the points (dutyCycle:0,
* boost:maxFiringBoost) and (dutyCycle:minDuty, boost:1.0).
*
* boostFactor
* ^
* maxBoost _ |
* |\
* | \
* 1 _ | \ _ _ _ _ _ _ _
* |
* +--------------------> activeDutyCycle
* |
* minActiveDutyCycle
*/
public void updateBoostFactors(Connections c) {
double[] activeDutyCycles = c.getActiveDutyCycles();
double[] minActiveDutyCycles = c.getMinActiveDutyCycles();
//Indexes of values > 0
int[] mask = ArrayUtils.where(minActiveDutyCycles, ArrayUtils.GREATER_THAN_0);
double[] boostInterim;
if(mask.length < 1) {
boostInterim = c.getBoostFactors();
}else{
double[] numerator = new double[c.getNumColumns()];
Arrays.fill(numerator, 1 - c.getMaxBoost());
boostInterim = ArrayUtils.divide(numerator, minActiveDutyCycles, 0, 0);
boostInterim = ArrayUtils.multiply(boostInterim, activeDutyCycles, 0, 0);
boostInterim = ArrayUtils.d_add(boostInterim, c.getMaxBoost());
}
ArrayUtils.setIndexesTo(boostInterim, ArrayUtils.where(activeDutyCycles, new Condition.Adapter © 2015 - 2025 Weber Informatics LLC | Privacy Policy